Fandi Gou

Modeling Deception in Multi-Robot Target-Attacker-Defender Game via Deep Reinforcement Learning

Deception is a crucial strategy in adversarial scenarios, yet its application in multi-agent confrontations remains understudied. This paper investigates deception in a multi-robot Target-Attacker-Defender (MR-TAD) game, where Attackers aim to capture Targets while evading Defenders. To model decept